ObjectiveTo perform a systematic review of studies using remote physical activity monitoring in neurological diseases, highlighting advances and determining gaps.MethodsStudies were systematically identified in PubMed/MEDLINE, CINAHL and SCOPUS from January 2004 to December 2014 that monitored physical activity for ≥24 hours in adults with neurological diseases. Studies that measured only involuntary motor activity (tremor, seizures), energy expenditure or sleep were excluded. Feasibility, findings, and protocols were examined.Results137 studies met inclusion criteria in multiple sclerosis (MS) (61 studies); stroke (41); Parkinson's Disease (PD) (20); dementia (11); traumatic brain injury (2) and ataxia (1). Physical activity levels measured by remote monitoring are consistently low in people with MS, stroke and dementia, and patterns of physical activity are altered in PD. In MS, decreased ambulatory activity assessed via remote monitoring is associated with greater disability and lower quality of life. In stroke, remote measures of upper limb function and ambulation are associated with functional recovery following rehabilitation and goal-directed interventions. In PD, remote monitoring may help to predict falls. In dementia, remote physical activity measures correlate with disease severity and can detect wandering.ConclusionsThese studies show that remote physical activity monitoring is feasible in neurological diseases, including in people with moderate to severe neurological disability. Remote monitoring can be a psychometrically sound and responsive way to assess physical activity in neurological disease. Further research is needed to ensure these tools provide meaningful information in the context of specific neurological disorders and patterns of neurological disability.

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Disability measures in multiple sclerosis (MS) rely heavily on ambulatory function, and current metrics fail to capture potentially important variability in walking behavior. We sought to determine whether remote step count monitoring using a consumer-friendly accelerometer (Fitbit Flex) can enhance MS disability assessment. 99 adults with relapsing or progressive MS able to walk C2-min were prospectively recruited. At 4 weeks, study retention was 97% and median Fitbit use was 97% of days. Substudy validation resulted in high interclass correlations between Fitbit, ActiGraph and manual step count tally during a 2-minute walk test, and between Fitbit and ActiGraph (ICC = 0.76) during 7-day home monitoring. Over 4 weeks of continuous monitoring, daily steps were lower in progressive versus relapsing MS (mean difference 2546 steps, p \ 0.01). Lower average daily step count was associated with greater disability on the Expanded Disability Status Scale (EDSS) (p \ 0.001). Within each EDSS category, substantial variability in step count was apparent (i.e., EDSS = 6.0 range 1097–7152). Step count demonstrated moderate-strong correlations with other walking measures. Lower average daily step count is associated with greater MS disability and captures important variability in real-world walking activity otherwise masked by standard disability scales, including the EDSS. These results support remote step count monitoring as an exploratory outcome in MS trials.

Importance Disability measures in multiple sclerosis (MS) fail to capture potentially important variability in walking behavior. More sensitive and ecologically valid outcome measures are needed to advance MS research. Objectives To assess continuous step count activity remotely among individuals with MS for 1 year and determine how average daily step count is associated with other measures of MS disability. Design, Setting, and Participants In a prospective longitudinal observational cohort study, 95 adults with relapsing or progressive MS who were able to walk more than 2 minutes with or without an assistive device were recruited between June 15, 2015, and August 8, 2016, and remotely monitored in their natural environment for 1 year. Patients were excluded if they had a clinical relapse within 30 days or comorbidity contributing to ambulatory impairment. Longitudinal analysis was performed from October 2017 to March 2018. Revised analysis was performed in December 2018. Intervention Activity monitoring of step count using a wrist-worn accelerometer. Main Outcomes and Measures Average daily step count compared with in-clinic assessments and patient-reported outcomes. Results Of the 95 participants recruited (59 women and 36 men; mean [SD] age, 49.6 [13.6] years [range, 22.0-74.0 years]), 35 (37%) had progressive MS, and the median baseline Expanded Disability Status Scale score was 4.0 (range, 0-6.5). At 1 year, 79 participants completed follow-up (83% retention). There was a modest reduction in accelerometer use during the 1 year of the study. A decreasing average daily step count during the study was associated with worsening of clinic-based outcomes (Timed 25-Foot Walk, β = −13.09; P < .001; Timed-Up-and-Go, β = −9.25; P < .001) and patient-reported outcomes (12-item Multiple Sclerosis Walking Scale, β = −17.96; P < .001). A decreasing average daily step count occurred even when the Expanded Disability Status Scale score remained stable, and 12 of 25 participants (48%) with a significant decrease in average daily step count during the study did not have a reduction on other standard clinic-based metrics. Participants with a baseline average daily step count below 4766 (cohort median) had higher odds of clinically meaningful disability (Expanded Disability Status Scale score) worsening at 1 year, adjusting for age, sex, and disease duration (odds ratio, 4.01; 95% CI, 1.17-13.78; P = .03). Conclusions and Relevance Continuous remote activity monitoring of individuals with MS for 1 year appears to be feasible. In this study, a decreasing average daily step count during a 1-year period was associated with worsening of standard ambulatory measures but could also occur even when traditional disability measures remained sta...

This paper compares the approach and resultant outcomes of item response models (IRMs) and classical test theory (CTT). First, it reviews basic ideas of CTT, and compares them to the ideas about using IRMs introduced in an earlier paper. It then applies a comparison scheme based on the AERA/APA/NCME 'Standards for Educational and Psychological Tests' to compare the two approaches under three general headings: (i) choosing a model; (ii) evidence for reliability--incorporating reliability coefficients and measurement error--and (iii) evidence for validity--including evidence based on instrument content, response processes, internal structure, other variables and consequences. An example analysis of a self-efficacy (SE) scale for exercise is used to illustrate these comparisons. The investigation found that there were (i) aspects of the techniques and outcomes that were similar between the two approaches, (ii) aspects where the item response modeling approach contributes to instrument construction and evaluation beyond the classical approach and (iii) aspects of the analysis where the measurement models had little to do with the analysis or outcomes. There were no aspects where the classical approach contributed to instrument construction or evaluation beyond what could be done with the IRM approach. Finally, properties of the SE scale are summarized and recommendations made.

LLLT confers clinically meaningful reductions in arm volume and pain in women with BCRL.

TKA is an effective procedure for improving ROM and decreasing functional deficits resulting from haemophilic arthropathy. Knee score data shows TKA improves overall function. This study guides clinicians regarding outcome expectations post-TKA in PWH.

This paper is the first of several papers designed to demonstrate how the application of item response models in the behavioral sciences can be used to enhance the conceptual and technical toolkit of researchers and developers and to understand better the psychometric properties of psychosocial measures. The papers all use baseline data from the Behavior Change Consortium data archive. This paper begins with an introduction to item response models, including both dichotomous and polytomous versions. The concepts of respondent and item location, model interpretation, standard errors and testing model fit are introduced and described. A sample analysis based on data from the self-efficacy scale is used to illustrate the concepts and techniques.